Date of Award

12-2025

Degree Type

Thesis

Degree Name

M.S.E.

Degree Program

Electrical Engineering

Department

Electrical Engineering

Major Professor

Alsamman, Abdul

Second Advisor

Charalampidis, Dimitrios

Third Advisor

Hoque, Tamjidul

Fourth Advisor

Jovanovich, Kim

Abstract

Segmentation of curvilinear structures such as water contours, cracks in cement, and vascular networks in biomedical imaging, poses unique challenges due to extreme class imbalance, irregular morphology, low contrast against complex backgrounds, and the need to preserve global connectivity while detecting fine-scale details. We propose a Multiscale Variational U-Net (MSVU-Net) architecture designed specifically to address these challenges. The model integrates multiscale convolutional filters to capture both global context and local detail, while embedding a variational model in the bottleneck layer to enhance structural representation. To mitigate class imbalance and improve fidelity, the network optimizes a hybrid loss function that combines pixel-level criteria with structural-level penalties. This dual-level supervision ensures accurate detection of sparse features while maintaining their continuity. Experimental evaluation on two benchmark datasets, water contour and cement crack datasets, demonstrates that the proposed framework consistently outperforms existing variational models and several state-of-the-art segmentation architectures.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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